Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online

Crystal Lee, Tanya Yang, Gabrielle Inchoco, Graham M. Jones, Arvind Satyanarayan

Sample counter-visualizations from the anti-mask user network. While there are meme-based visualizations, anti-maskers on Twitter adopt the same visual vocabulary as visualization experts and the mainstream media.

Abstract

Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government's pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.

Citation

Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online

Crystal Lee, Tanya Yang, Gabrielle Inchoco, Graham M. Jones, Arvind Satyanarayan

ACM Human Factors in Computing Systems (CHI), 2021.

Bibtex

@inproceedings{2021-viral-visualizations,
 title = {{Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online}},
 author = {Crystal Lee AND Tanya Yang AND Gabrielle Inchoco AND Graham M. Jones AND Arvind Satyanarayan},
 booktitle = {ACM Human Factors in Computing Systems (CHI)},
 year = {2021},
 url = {http://vis.csail.mit.edu/pubs/viral-visualizations}
}